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README.md
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@@ -21,7 +21,7 @@ This is a [TRL language model](https://github.com/huggingface/trl). It has been
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is a method to align models with a particular kind of data. RLHF creates a latent reward model using human feedback and finetunes
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a model using Proximal Policy Optimization. RLAIF on the other hand replaces human feedback with a high-performance AI agent. The model
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has been fine-tuned on the [Social Reasoning Dataset](https://huggingface.co/datasets/ProlificAI/social-reasoning-rlhf/viewer/default/train?p=38&row=3816) by
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ProlificAI for 191 steps and 1 epoch using the Proximal Policy Optimisation (PPO) algorithm. The [Roberta hate
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model was used as the Proximal Policy Optimisation (PPO) reward model.
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The power of this model lies in its size; it is barely 500 MBs in size and performs well given its size. The intended use of this model should be conversation, text generation, or context-based Q&A.
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is a method to align models with a particular kind of data. RLHF creates a latent reward model using human feedback and finetunes
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a model using Proximal Policy Optimization. RLAIF on the other hand replaces human feedback with a high-performance AI agent. The model
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has been fine-tuned on the [Social Reasoning Dataset](https://huggingface.co/datasets/ProlificAI/social-reasoning-rlhf/viewer/default/train?p=38&row=3816) by
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ProlificAI for 191 steps and 1 epoch using the Proximal Policy Optimisation (PPO) algorithm. The [Roberta hate text detection](https://huggingface.co/facebook/roberta-hate-speech-dynabench-r4-target)
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model was used as the Proximal Policy Optimisation (PPO) reward model.
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The power of this model lies in its size; it is barely 500 MBs in size and performs well given its size. The intended use of this model should be conversation, text generation, or context-based Q&A.
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